Estimating the Frequencies of Maximal Theta-Gamma Coupling in EEG during the N-Back Task: Sensitivity to Methodology and Temporal Instability

IF 1.8 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Algorithms Pub Date : 2023-11-27 DOI:10.3390/a16120540
D. Sinitsyn, A. Poydasheva, I. Bakulin, A. Zabirova, D. Lagoda, Natalia Suponeva, M. Piradov
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Abstract

Phase-amplitude coupling (PAC) of theta and gamma rhythms of the brain has been observed in animals and humans, with evidence of its involvement in cognitive functions and brain disorders. This motivates finding individual frequencies of maximal theta-gamma coupling (TGC) and using them to adjust brain stimulation. This use implies the stability of the frequencies at least during the investigation, which has not been sufficiently studied. Meanwhile, there is a range of available algorithms for PAC estimation in the literature. We explored several options at different steps of the calculation, applying the resulting algorithms to the EEG data of 16 healthy subjects performing the n-back working memory task, as well as a benchmark recording with previously reported strong PAC. By comparing the results for the two halves of each session, we estimated reproducibility at a time scale of a few minutes. For the benchmark data, the results were largely similar between the algorithms and stable over time. However, for the EEG, the results depended substantially on the algorithm, while also showing poor reproducibility, challenging the validity of using them for personalizing brain stimulation. Further research is needed on the PAC estimation algorithms, cognitive tasks, and other aspects to reliably determine and effectively use TGC parameters in neuromodulation.
估计 N-Back 任务期间脑电图中最大 Theta-Gamma 耦合的频率:对方法和时间不稳定性的敏感性
在动物和人类身上观察到了大脑θ和γ节律的相位-振幅耦合(PAC),有证据表明它与认知功能和脑部疾病有关。这就促使人们寻找θ-γ最大耦合(TGC)的个别频率,并利用它们来调整大脑刺激。这种用途意味着至少在调查期间频率要保持稳定,而这一点尚未得到充分研究。同时,文献中也有一系列可用的 PAC 估算算法。我们在计算的不同步骤探索了几种方案,并将得出的算法应用于 16 名健康受试者执行 n-back 工作记忆任务的脑电图数据,以及之前报道过的具有较强 PAC 的基准记录。通过比较每节课两部分的结果,我们估计了几分钟时间尺度内的再现性。就基准数据而言,两种算法的结果基本相似,而且随着时间的推移趋于稳定。然而,对于脑电图,结果在很大程度上取决于算法,同时也显示出较低的可重复性,这对使用这些算法进行个性化脑刺激的有效性提出了挑战。要在神经调控中可靠地确定并有效地使用 TGC 参数,还需要对 PAC 估算算法、认知任务和其他方面进行进一步研究。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Algorithms
Algorithms Mathematics-Numerical Analysis
CiteScore
4.10
自引率
4.30%
发文量
394
审稿时长
11 weeks
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